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A study on Deep Learning-based Stock Price Prediction using News Sentiment Analysis

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2022, 27(8), pp.31-39
  • DOI : 10.9708/jksci.2022.27.08.031
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : July 13, 2022
  • Accepted : August 3, 2022
  • Published : August 31, 2022

Doo-Won Kang 1 So-Yeop Yoo 1 Ha-Young Lee 1 Ok-Ran Jeong 1

1가천대학교

Accredited

ABSTRACT

Stock prices are influenced by a number of external factors, such as laws and trends, as well as number-based internal factors such as trading volume and closing prices. Since many factors affect stock prices, it is very difficult to accurately predict stock prices using only fragmentary stock data. In particular, since the value of a company is greatly affected by the perception of people who actually trade stocks, emotional information about a specific company is considered an important factor. In this paper, we propose a deep learning-based stock price prediction model using sentiment analysis with news data considering temporal characteristics. Stock and news data, two heterogeneous data with different characteristics, are integrated according to time scale and used as input to the model, and the effect of time scale and sentiment index on stock price prediction is finally compared and analyzed. Also, we verify that the accuracy of the proposed model is improved through comparative experiments with existing models.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.